Modeling Research Of UAV Road Condition Monitoring System Based On Probability Model Checking

(1)With the rapid development of intelligent technology,UAV,unmanned vehicle,intelligent robot and so on can accomplish the task well in the dangerous area.Because unmanned technology saves manpower and material resources and liberates people from dangerous tasks,unmanned technology has become a hot topic in research.However,although the ability of unmanned technology autonomy is constantly improving,it always requires "humanintheloop" monitoring,which is still "platform nobody,system someone".Therefore,when UAV’s perform tasks in complex environments,they need the assistance of operators to make the next path selection.Because in complex task scenarios,different path choices and operators in workloads the characteristics of fatigue degree and other aspects will have a significant impact on the efficiency of completing the task.therefore,this paper takes the unmanned aerial vehicle to perform the road condition monitoring task as an example through the analysis of different scenarios,extends the markov decision process,establishes MDP(corresponding markov decision process model)model,and uses PRISM formal modeling tools to quantitatively analyze and verify the path selection and operator characteristics of the UAV mobile model.The main contributions made in this paper include the following:(2)This paper studies the uncertainty of the path when the fixed scene unmanned aerial vehicle interacts with the operator and the influence of the operator characteristics on the performance of the unmanned aerial vehicle.Taking the unmanned aerial vehicle as an example to study the scene of road network monitoring,this paper puts forward the operator’s method of processing the monitoring point and the non-monitoring point in the process of image processing and the strategy of avoiding the dangerous area.A Markov decision-making process model is established by using PRISM tools to verify the multi-objective attributes.Formal verification results show that the above methods reduce the workload of operators and increase the number of operators efficiency and safety of man-machine,and provide an effective way for the establishment of UAV flight safety model.(3)The moving model of the UAV variable area scenario for operator collaboration is studied because operator characteristics and UAV path selection will affect system performance and operator characteristics are co-operated by external factors(workload)and internal factors(fatigue and accuracy).Therefore,a UAV path optimization algorithm and obstacle avoidance strategybased on probabilistic model detection are proposed to model and analyze different UAV task monitoring scenarios as examples.According to different task scenarios,Markov decision-making process model is established by using PRISM tools,and different optimization algorithms,different direction selection and different operator characteristics are advanced line contrast validation.(4)Modeling and analysis of scenarios that ensure UAV can continue to perform monitoring tasks in extreme environments,feasibility analysis validation,and a comparative analysis of the maximum probability of determining the extreme environment and normal environment to complete the task in time.Modeling and analysis of road condition monitoring scenarios with different sizes and requirements in this paper improves the working efficiency of UAV path selection,enhances the security of the system,and provides help and reference value for the influence of operator characteristics on the system and UAV modeling methods,which has certain research significance.